Vocational education practical training result evaluation method and system based on machine learning

Abstract

The invention provides a vocational education practical training result evaluation method and system based on machine learning, and the method comprises the steps: determining students carrying out vocational education practical training result evaluation, and obtaining target evaluation students; based on the target evaluation student, education training information is obtained through machine learning, and target student education training information is obtained; performing automatic evaluation on the target student education practical training information by adopting an artificial intelligence evaluation analysis model to obtain an evaluation result of a target evaluation student; and performing comprehensive result analysis according to the evaluation result of the target evaluation student to obtain vocational education practical training result evaluation information. The vocational education training result artificial intelligence evaluation is realized in a machine learning mode, manpower can be saved, vocational education training result evaluation can be completed in a short time, subjective factors are reduced to participate in evaluation, and the vocational education training result evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a vocational education training result evaluation method and system based on machine learning. Background Art

[0002] Vocational education training refers to the teaching activities that organize students to carry out actual operations and practical exercises inside and outside the school during the vocational education teaching process, according to the professional training objectives and teaching plan requirements. The main purpose of the training is to enable students to apply the theoretical knowledge they have learned in a simulated real working environment to complete certain tasks independently or collaboratively, so as to improve students' hands-on ability, innovation ability and comprehensive quality. Usually, after the vocational education training is completed, an evaluation and analysis of the results of the vocational education training will be conducted to clarify the effectiveness of the vocational education training.

[0003] At present, the evaluation of vocational education training often requires a lot of manpower and time, and the evaluation process is easily affected by subjective factors, which leads to inaccurate evaluation of vocational education training results. Therefore, the present invention proposes a vocational education training result evaluation method and system based on machine learning, which adopts machine learning to realize artificial intelligence evaluation of vocational education training results, which can not only save manpower and complete the evaluation of vocational education training results in a shorter time, but also reduce the participation of subjective factors in the evaluation and improve the accuracy of vocational education training result evaluation. Summary of the invention

[0004] The purpose of the present invention is to provide a vocational education training results evaluation method and system based on machine learning to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for evaluating vocational education training results based on machine learning, comprising:

[0006] Determine the students who will be evaluated for vocational education and training results, and obtain the target evaluation students;

[0007] Based on the target evaluation, students acquire educational training information through machine learning to obtain educational training information of target students;

[0008] An artificial intelligence evaluation and analysis model is used to automatically evaluate the educational training information of target students to obtain the evaluation results of the target evaluation students;

[0009] A comprehensive result analysis is conducted based on the evaluation results of the target evaluation students to obtain the evaluation information of the vocational education training results.

[0010] Furthermore, when determining students for vocational education training result evaluation, feature extraction is performed on the students participating in the vocational education training to obtain the first feature information of the students and obtain a set of student feature information. Then, the first feature information of the students is analyzed in the set of student feature information to determine whether the same features exist. When the first feature information of the students has the same features, the corresponding students are determined based on the same first feature information of the students, and the second feature is obtained for the corresponding students to obtain the second feature information of the students. Then, it is analyzed and determined whether the second feature information of the students is the same. If the second feature information of the students is the same, the third feature is obtained until the feature information is different.

[0011] Furthermore, based on the target evaluation, students acquire educational training information through machine learning, including:

[0012] Conduct preliminary analysis and judgment on the education and training information to determine whether the education and training information is standardized and obtain preliminary analysis and judgment results;

[0013] When there is non-standardization in the education and training information according to the preliminary analysis and judgment results, the non-standardized information is screened and standardized to obtain standardized education and training information;

[0014] Determine the characteristic information of the target evaluation students and make the characteristic information clear;

[0015] Based on the feature information, a machine learning model is used to identify the standardized educational training information, filter out the educational training information about the target evaluation students, and obtain the educational training information of the target students.

[0016] Furthermore, when identifying in the standardized educational training information based on the feature information through the machine learning model, the number of feature information is analyzed. When the number of feature information is unique, the feature information is the first feature information of the student. Based on the first feature information of the student, the machine learning model is used to identify, and the educational training information about the target evaluation student is screened out to obtain the educational training information of the target student; when the number of feature information is not unique, information set matching is performed according to the student's N-1th feature information. If a matching result exists, the matching result is identified based on the student's Nth feature information through the machine learning model, and the educational training information about the target evaluation student is screened out to obtain the educational training information of the target student. If no matching result exists, information set matching is performed according to the student's N-2th feature information, and it is analyzed whether there is a match. When a matching result exists, the machine learning model is used to identify based on the student's N-1th feature information, and the educational training information about the target evaluation student is screened out to obtain the identification and screening result. Then, the identification and screening result is marked and stored in combination with the student's N-1th feature information, and at the same time, the identification is performed based on the student's Nth feature information through the machine learning model in the identification and screening result to obtain the educational training information of the target student.

[0017] Further, the artificial intelligence evaluation and analysis model includes: a first evaluation unit, a second evaluation unit, a third evaluation unit and a summary evaluation unit;

[0018] The first evaluation unit is used to identify and extract the student discipline record information in the target student education and training information, and evaluate and analyze the student discipline record information according to the discipline regulations to obtain a first evaluation result;

[0019] The second evaluation unit is used to identify and extract the student practical learning information from the target student's educational training information to obtain a second evaluation result;

[0020] The third evaluation unit is used to identify and extract the student assessment record information in the target student's education and training information, and to synthesize the individual assessment results based on the student assessment record information to obtain a third evaluation result;

[0021] The summary evaluation unit is used to integrate data according to the first evaluation result, the second evaluation result and the third evaluation result to obtain the evaluation result of the target evaluation student.

[0022] Furthermore, the summary evaluation unit integrates data according to the first evaluation result, the second evaluation result and the third evaluation result, including:

[0023] Obtaining occupational information for vocational education and training;

[0024] Analyze occupational characteristics based on occupational information and determine the focus of vocational education and training;

[0025] Conduct result weight analysis based on the focus of vocational education and training and determine the weight value;

[0026] The first evaluation result, the second evaluation result and the third evaluation result are integrated with the weight value to determine the comprehensive evaluation value and obtain the evaluation result of the target evaluation student.

[0027] Furthermore, a comprehensive result analysis is conducted based on the evaluation results of the target evaluation students, including:

[0028] Perform fuzzy evaluation conversion on the evaluation results of the target evaluation students to obtain the fuzzy evaluation results of the target evaluation students;

[0029] Determine the distribution of vocational education training results according to the fuzzy evaluation results of the target evaluation students, and obtain the distribution results of vocational education training results;

[0030] A comprehensive analysis is conducted based on the distribution of vocational education training results to obtain evaluation information on vocational education training results.

[0031] Furthermore, the vocational education training result evaluation information includes: text information and image information, and when a comprehensive analysis is performed according to the distribution results of the vocational education training results, it includes:

[0032] Determine the image type of the image information and clarify the target image type;

[0033] According to the distribution results of vocational education training results, images are generated according to the target image type to obtain vocational education training result evaluation image information;

[0034] An intelligent analysis model is used to identify and analyze the image information of vocational education training results evaluation to obtain text information of vocational education training results evaluation.

[0035] Furthermore, after the image is generated according to the target image type based on the distribution result of the vocational education training results, image processing is also performed on the vocational education training results evaluation image information, including:

[0036] According to the fuzzy evaluation results of the target evaluation students, information division processing is performed on the evaluation results of the target evaluation students to obtain multiple information data blocks;

[0037] Establish the link relationship between the information data block and the fuzzy evaluation results of the target evaluation students, and determine the first-level link;

[0038] The information data block is matched with the vocational education training result evaluation image information, and a link relationship between the information data block and the vocational education training result evaluation image information is established according to the matching result to determine the second-level link.

[0039] A vocational education training result evaluation system based on machine learning, comprising: a target confirmation module, an information acquisition module, an intelligent analysis module and a comprehensive evaluation module;

[0040] The target confirmation module is used to determine the students who will be evaluated for the vocational education training results and obtain the target evaluation students;

[0041] The information acquisition module is used to acquire educational training information based on target evaluation students through machine learning to obtain educational training information of target students;

[0042] The intelligent analysis module is used to use an artificial intelligence evaluation and analysis model to automatically evaluate the educational training information of the target students and obtain the evaluation results of the target evaluation students;

[0043] The comprehensive evaluation module is used to perform comprehensive result analysis based on the evaluation results of the target evaluation students to obtain vocational education training result evaluation information.

[0044] The present invention adopts machine learning to realize artificial intelligence evaluation of vocational education training results, which can not only save manpower and complete the evaluation of vocational education training results in a shorter time, but also reduce the participation of subjective factors in the evaluation and improve the accuracy of the evaluation of vocational education training results.

[0045] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 It is a schematic diagram of the steps of the vocational education training result evaluation method of the present invention;

[0049] Figure 2 This is a schematic diagram of step 2 in the vocational education training result evaluation method of the present invention;

[0050] Figure 3 It is a schematic diagram of the artificial intelligence evaluation and analysis model of step three in the vocational education training result evaluation method of the present invention;

[0051] Figure 4 It is a schematic diagram of the vocational education training results evaluation system described in the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a vocational education training result evaluation method based on machine learning, including:

[0054] Step 1: Determine the students who will be evaluated for vocational education training results and obtain the target evaluation students;

[0055] Step 2: Based on the target evaluation, students are evaluated and educational training information is obtained through machine learning to obtain educational training information of target students;

[0056] Step 3: Use an artificial intelligence evaluation and analysis model to automatically evaluate the target student's educational training information to obtain the evaluation results of the target evaluation student;

[0057] Step 4: Conduct a comprehensive result analysis based on the evaluation results of the target evaluation students to obtain the evaluation information of the vocational education training results.

[0058] In the above technical solution, the number of target evaluation students is not unique, and can be one or more.

[0059] In the above technical solution, the vocational education training results evaluation information is the overall rating and analysis information of the vocational education training results.

[0060] In the above technical solution, the target student education and training information refers to the education and training information about the target evaluation student.

[0061] The above technical solution realizes the effect analysis of vocational education training through the evaluation of vocational education training results, so as to understand the effect of vocational education training and clarify the role of vocational education training. By determining the target evaluation students, when evaluating the results of vocational education training, students are used as the evaluation objects for individual and overall evaluation, which improves the comprehensiveness of the evaluation of vocational education training results. In addition, machine learning is used to obtain educational training information for target evaluation students, and there is no need to manually obtain the required target information from a large amount of vocational education training information, which saves manpower consumption, reduces the redundancy of irrelevant information, and provides convenience for subsequent automated evaluation. In addition, an artificial intelligence evaluation and analysis model is used to automatically evaluate the educational training information of target students, improve the evaluation efficiency, and make it possible to conduct comprehensive result analysis in a shorter time, thereby improving the efficiency of obtaining vocational education training result evaluation information.

[0062] In one embodiment provided by the present invention, when determining students for vocational education training results evaluation, feature extraction is performed on the students participating in the vocational education training to obtain the first feature information of the students and obtain a set of student feature information. Then, the first feature information of the students is analyzed in the set of student feature information to determine whether the same features exist. When the first feature information of the students has the same features, the corresponding students are determined based on the same first feature information of the students, and the second feature is obtained for the corresponding students to obtain the second feature information of the students. Then, it is analyzed and determined whether the second feature information of the students is the same. If the second feature information of the students is the same, the third feature is obtained until the feature information is different.

[0063] In the above technical solution, when a student participating in vocational education training has multiple characteristic information, the student characteristic information includes: first characteristic information, second characteristic information, third characteristic information...

[0064] In the above technical solution, the characteristic information includes: name, age, time of employment, length of employment, etc.

[0065] In the above technical solution, when the target evaluation student is obtained, characteristic information of the target evaluation student is obtained.

[0066] The above-mentioned technical scheme extracts features from students participating in vocational education and training so that when acquiring education and training information, the education and training information of students can be distinguished according to the characteristic information, thereby making independent evaluations according to students during evaluation, reducing the redundancy of irrelevant information, reducing the degree of confusion in the evaluation process, and improving the efficiency of automated evaluation.

[0067] like Figure 2 As shown, in one embodiment provided by the present invention, obtaining educational training information through machine learning based on target evaluation of students includes:

[0068] S1. Conduct preliminary analysis and judgment on the education and training information to determine whether the education and training information is standardized and obtain preliminary analysis and judgment results;

[0069] S2. When there is non-standardization in the education and training information according to the preliminary analysis and judgment results, the non-standardized information is screened and standardized processing is performed on the non-standardized information to obtain standardized education and training information;

[0070] S3, determine the characteristic information of the target evaluation students and clarify the characteristic information;

[0071] S4. Based on the feature information, the standardized educational training information is identified through a machine learning model, and the educational training information about the target evaluation students is screened out to obtain the educational training information of the target students.

[0072] In the above technical solution, the machine learning model is a pre-optimized and trained information recognition model.

[0073] In the above technical solution, if the preliminary analysis and judgment result shows that all the educational training information is standardized, there is no need to perform standardization processing and the subsequent steps can be carried out directly.

[0074] In the above technical solution, the characteristic information includes: one or more of the student's first characteristic information, the student's second characteristic information, the student's third characteristic information, ...

[0075] The above-mentioned technical scheme makes a preliminary analysis and judgment on the educational training information so that when the educational training information is acquired through machine learning, the educational training information has a standardized form, which provides convenience for machine learning and avoids the inability of machine learning to recognize the information due to non-standard information, thereby causing large machine learning errors. The accuracy of the acquisition of educational training information is ensured, and the standardized educational training information is identified through a machine learning model based on feature information. Not only does it use the machine learning model to efficiently identify and screen information, reduce time consumption, and improve the efficiency of acquiring educational training information for target students, but it also uses feature information for distinction, which can effectively remove redundant educational training information that is not related to the target evaluation students, and provide convenience for information processing of subsequent artificial intelligence evaluation and analysis models.

[0076] In one embodiment provided by the present invention, when identifying in the standardized educational practice information based on the feature information through the machine learning model, the number of feature information is analyzed. When the number of feature information is unique, the feature information is the first feature information of the student. Based on the first feature information of the student, the machine learning model is used to identify, and the educational practice information about the target evaluation student is screened out to obtain the educational practice information of the target student; when the number of feature information is not unique, information set matching is performed according to the student's N-1th feature information. If there is a matching result, the matching result is identified based on the student's Nth feature information through the machine learning model, and the educational practice information about the target evaluation student is screened out to obtain the educational practice information of the target student. If there is no matching result, the information set matching is performed according to the student's N-2th feature information, and it is analyzed whether there is a match. When there is a matching result, the machine learning model is used to identify based on the student's N-1th feature information, and the educational practice information about the target evaluation student is screened out to obtain the identification and screening result. Then, the identification and screening result is marked and stored in combination with the student's N-1th feature information, and the identification and screening result is identified based on the student's Nth feature information through the machine learning model to obtain the educational practice information of the target student.

[0077] In the above technical solution, the characteristic information includes: the first characteristic information of the student, the second characteristic information of the student, ..., the Nth characteristic information of the student.

[0078] In the above technical solution, when N-2 is less than 1, that is, the feature information only includes the student's first feature information and the student's second feature information, when the information set is matched according to the student's N-1th feature information, if there is no matching result, the educational training information about the target evaluation student is directly identified through the machine learning model based on the student's first feature information, and the identification and screening results are marked and stored in combination with the student's first feature information.

[0079] In the above technical scheme, information set matching is performed according to the student's N-2th characteristic information, and when analyzing whether there is a match, if there is still no matching result, information set matching is performed according to the student's N-3th characteristic information, and when there is a matching result, identification is performed based on the student's N-2th characteristic information through a machine learning model, and the educational training information about the target evaluation student is screened out to obtain a first identification and screening result, and then the first identification and screening result is marked and stored in combination with the student's N-2th characteristic information, and at the same time, the identification and screening result is identified based on the student's N-1th characteristic information through a machine learning model to obtain a second identification and screening result, and then the second identification and screening result is marked and stored in combination with the student's N-1th characteristic information, and at the same time, the identification and screening result is identified based on the student's Nth characteristic information through a machine learning model to obtain the target student's educational training information. If there is still no matching result when information set matching is performed according to the student's N-3th characteristic information, the above process is repeated until the student characteristic information matches in the information set or there is no matching result for all student characteristic information.

[0080] In the above technical solution, when information set matching is performed, matching is performed in the data information stored in the tag.

[0081] The above technical solution analyzes the number of feature information to perform different identification and screening according to the number of feature information, thereby avoiding identification and screening errors caused by the same feature information, and ensuring the accuracy of the target student's educational training information. Moreover, when the number of feature information is not unique, through marked storage, when the target evaluation students with the same feature information are obtained, identification and screening can be directly based on the common data set when acquiring the target student's educational training information, thereby improving the efficiency of machine learning and reducing unnecessary waste of time.

[0082] like Figure 3 As shown, in one embodiment provided by the present invention, the artificial intelligence evaluation and analysis model includes: a first evaluation unit, a second evaluation unit, a third evaluation unit and a summary evaluation unit;

[0083] The first evaluation unit is used to identify and extract the student discipline record information in the target student education and training information, and evaluate and analyze the student discipline record information according to the discipline regulations to obtain a first evaluation result;

[0084] The second evaluation unit is used to identify and extract the student practical learning information from the target student's educational training information to obtain a second evaluation result;

[0085] The third evaluation unit is used to identify and extract the student assessment record information in the target student's education and training information, and to synthesize the individual assessment results based on the student assessment record information to obtain a third evaluation result;

[0086] The summary evaluation unit is used to integrate data according to the first evaluation result, the second evaluation result and the third evaluation result to obtain the evaluation result of the target evaluation student.

[0087] In the above technical solution, the first evaluation result, the second evaluation result and the third evaluation result are all numerical results.

[0088] In the above technical solution, when an artificial intelligence evaluation and analysis model is used to perform automated evaluation on the target students' educational training information, the first evaluation unit, the second evaluation unit, and the third evaluation unit can be performed simultaneously, and the first evaluation result, the second evaluation result, and the third evaluation result can be obtained at the same time.

[0089] The above technical scheme realizes the automated evaluation of target students through the artificial intelligence evaluation and analysis model, so that the evaluation results of target evaluation students can be obtained more efficiently, and the intelligent performance of the vocational education training results evaluation method can be improved. In addition, the first evaluation unit, the second evaluation unit, and the third evaluation unit are used in the artificial intelligence evaluation and analysis model to enable the first evaluation result, the second evaluation result, and the third evaluation result to be obtained simultaneously, thereby reducing time consumption, improving the efficiency of the artificial intelligence evaluation and analysis model, and thereby improving the efficiency of the vocational education training results evaluation method.

[0090] In one embodiment provided by the present invention, the summary evaluation unit integrates data according to the first evaluation result, the second evaluation result and the third evaluation result, including:

[0091] Obtaining occupational information for vocational education and training;

[0092] Analyze occupational characteristics based on occupational information and determine the focus of vocational education and training;

[0093] Conduct result weight analysis based on the focus of vocational education and training and determine the weight value;

[0094] The first evaluation result, the second evaluation result and the third evaluation result are integrated with the weight value to determine the comprehensive evaluation value and obtain the evaluation result of the target evaluation student.

[0095] In the above technical solution, occupational characteristics analysis is performed on occupational information, including: performing occupational content analysis based on occupational information to determine occupational work content; performing multi-angle evaluation based on occupational work content to obtain multi-angle evaluation information; wherein the multi-angles include: job responsibilities, professional knowledge, communication skills, interpersonal relationships, time management, professional qualifications, etc.; performing correlation analysis on the multi-angle evaluation information and the evaluation of vocational education and training to obtain multiple correlation information sets, wherein the correlation information sets are consistent with the evaluation of vocational education and training, and are divided into a first correlation information set, a second correlation information set and a third correlation information set according to the major categories of the evaluation of vocational education and training; The number of multi-angle evaluation information is determined in the associated information set. When there is only one angle evaluation information in the information set, the key analysis data of the associated information set is obtained by combining the angle evaluation information with the degree of association between the angle evaluation information and the associated information set. When there are multiple angle evaluation information in the information set, the angle evaluation information is analyzed separately in combination with the degree of association between the angle evaluation information and the associated information set to obtain multiple sub-analysis data. The sub-analysis data are then integrated to obtain the key analysis data of the associated information set. Relative analysis is performed based on the key analysis data of the associated information set to determine the focus of vocational education training.

[0096] In the above technical solutions, different occupations place different emphasis on the first evaluation results, the second evaluation results and the third evaluation results in vocational education and training.

[0097] In the above technical solution, the weight values ​​include: the weight value of the first evaluation result, the weight value of the second evaluation result and the weight value of the third evaluation result.

[0098] In the above technical solution, the evaluation result of the target evaluation student is a numerical value.

[0099] The above technical solution integrates data by combining occupational information, so that the vocational education training results evaluation method can be appropriately adjusted according to different occupations, reducing the error of the evaluation results of the target evaluation students, and providing a guarantee for the accuracy of the vocational education training results evaluation information.

[0100] In one embodiment provided by the present invention, comprehensive result analysis is performed based on the evaluation results of the target evaluation students, including:

[0101] Perform fuzzy evaluation conversion on the evaluation results of the target evaluation students to obtain the fuzzy evaluation results of the target evaluation students;

[0102] Determine the distribution of vocational education training results according to the fuzzy evaluation results of the target evaluation students, and obtain the distribution results of vocational education training results;

[0103] A comprehensive analysis is conducted based on the distribution of vocational education training results to obtain evaluation information on vocational education training results.

[0104] In the above technical solution, the fuzzy evaluation includes: excellent, good, fair, pass and fail.

[0105] In the above technical solution, when fuzzy evaluation conversion is performed on the evaluation results of the target evaluation students, information conversion is performed according to the fuzzy evaluation conversion rules, for example: greater than 90 is excellent, 90-80 is good, 80-70 is medium, 70-60 is passing, and less than 60 is failing.

[0106] The above technical solution simplifies the process of comprehensive result analysis by converting the evaluation results of the target evaluation students into fuzzy evaluation, reduces the complexity of the comprehensive result analysis, avoids analysis confusion and analysis errors caused by excessive data information, and ensures the accuracy of vocational education training result evaluation information.

[0107] In one embodiment provided by the present invention, the vocational education training result evaluation information includes: text information and image information, and when a comprehensive analysis is performed according to the distribution result of the vocational education training result, it includes:

[0108] Determine the image type of the image information and clarify the target image type;

[0109] According to the distribution results of vocational education training results, images are generated according to the target image type to obtain vocational education training result evaluation image information;

[0110] An intelligent analysis model is used to identify and analyze the image information of vocational education training results evaluation to obtain text information of vocational education training results evaluation.

[0111] In the above technical solution, the image types include: pie chart, bar chart, area chart, etc.

[0112] In the above technical solution, the intelligent analysis model includes: an identification unit, an analysis unit and a verification unit. The intelligent analysis model identifies and analyzes the image information for evaluating the results of vocational education training, including:

[0113] The vocational education training result evaluation image information is identified by the identification unit, and the information of the vocational education training result evaluation image information is read to obtain the representation information of the vocational education training result evaluation image information;

[0114] Using the analysis unit to perform an artificial intelligence overview on the representation information of the vocational education training result evaluation image information to obtain first evaluation text information, and at the same time perform feature analysis on the representation information of the vocational education training result evaluation image information to obtain the vocational education training result evaluation image features, and perform artificial intelligence description based on the vocational education training result evaluation image features to obtain second evaluation text information, and then obtain the vocational education training result evaluation text initial information based on the first text information and the second text information;

[0115] The verification unit shall review and verify the initial information of the vocational education training results evaluation text. If the review and verification passes, the initial information of the vocational education training results evaluation text shall be the final vocational education training results evaluation text information. If the review and verification fails, the initial information of the vocational education training results evaluation text shall be intelligently corrected, and the corrected initial information of the vocational education training results evaluation text shall be used as the final vocational education training results evaluation text information.

[0116] In the above technical solution, when image generation is performed according to the target image type based on the distribution results of vocational education training results, image generation is performed with the help of an image generation tool.

[0117] The above-mentioned technical scheme makes the text information of vocational education training result evaluation more comprehensive and intuitive through text information and image information. At the same time, it also allows relevant personnel to not only directly clarify the status of vocational education training results through text information, but also understand the vocational education training results through image information by themselves. In addition, an intelligent analysis model is used for identification and analysis, without the need for subjective analysis by humans, thus saving time. At the same time, it also reduces the error impact caused by subjective factors, thereby improving the intelligence of vocational education training result evaluation.

[0118] In one embodiment provided by the present invention, after image generation is performed according to the target image type based on the vocational education training result distribution result, image processing is also performed on the vocational education training result evaluation image information, including:

[0119] According to the fuzzy evaluation results of the target evaluation students, information division processing is performed on the evaluation results of the target evaluation students to obtain multiple information data blocks;

[0120] Establish the link relationship between the information data block and the fuzzy evaluation results of the target evaluation students, and determine the first-level link;

[0121] The information data block is matched with the vocational education training result evaluation image information, and a link relationship between the information data block and the vocational education training result evaluation image information is established according to the matching result to determine the second-level link.

[0122] In the above technical solution, one student corresponds to one link point, and the vocational education training information of the corresponding student can be retrieved and viewed through the link point in the information data block.

[0123] In the above technical scheme, when establishing a link relationship between an information data block and a fuzzy evaluation result of a target evaluation student, it includes: determining the student corresponding to the information data block; retrieving data information for the student, obtaining data information of the student during the vocational education training process, and obtaining a student data information database, and at the same time retrieving characteristic information for the student to obtain student feature information; establishing a link point in the information data block corresponding to the student according to the student feature information, determining the student link point, and creating a trust relationship based on the student link point, and then linking the student link point to the corresponding student data information database.

[0124] In the above technical solution, the information data block corresponds to the image block of the vocational education training result evaluation image information, and each information data block contains the corresponding student.

[0125] In the above technical solution, image processing refers to creating a link relationship based on the evaluation of image information based on the results of vocational education training.

[0126] The above-mentioned technical solution enriches the vocational education training result evaluation image information through image processing, so that more vocational education training result information can be further viewed based on the image, and then relevant personnel can further view specific vocational education training result information or verify the vocational education training result information based on the vocational education training result evaluation image information, providing convenience for relevant personnel.

[0127] like Figure 4 As shown, an embodiment of the present invention provides a vocational education training result evaluation system based on machine learning, including: a target confirmation module, an information acquisition module, an intelligent analysis module and a comprehensive evaluation module;

[0128] The target confirmation module is used to determine the students who will be evaluated for the vocational education training results and obtain the target evaluation students;

[0129] The information acquisition module is used to acquire educational training information based on target evaluation students through machine learning to obtain educational training information of target students;

[0130] The intelligent analysis module is used to use an artificial intelligence evaluation and analysis model to automatically evaluate the educational training information of the target students and obtain the evaluation results of the target evaluation students;

[0131] The comprehensive evaluation module is used to perform comprehensive result analysis based on the evaluation results of the target evaluation students to obtain vocational education training result evaluation information.

[0132] The above technical solution realizes the effect analysis of vocational education training results through the vocational education training result evaluation system, so as to understand the effect of vocational education training and clarify the role of vocational education training. The target evaluation students are determined through the target confirmation module, so that when evaluating the vocational education training results, the students are used as the evaluation objects for individual and overall evaluation, which improves the comprehensiveness of the evaluation of vocational education training results. In addition, the information acquisition module uses machine learning to obtain educational training information for target evaluation students, and there is no need to manually obtain the required target information from a large amount of vocational education training information, which saves manpower consumption, reduces the redundancy of irrelevant information, and provides convenience for the subsequent automatic evaluation of the intelligent analysis module. In addition, the intelligent analysis module uses an artificial intelligence evaluation and analysis model to automatically evaluate the target student's educational training information, improves the evaluation efficiency, and enables the comprehensive evaluation module to perform comprehensive result analysis in a relatively short time, thereby improving the efficiency of obtaining vocational education training result evaluation information.

[0133] Those skilled in the art should understand that the first and second in the present invention merely refer to different application stages.

[0134] Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0135] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A vocational education training result evaluation method based on machine learning, characterized in that: include: Determine the students who will be evaluated for vocational education and training results, and obtain the target evaluation students; Based on the target evaluation, students acquire educational training information through machine learning to obtain educational training information of target students; An artificial intelligence evaluation and analysis model is used to automatically evaluate the educational training information of target students to obtain the evaluation results of the target evaluation students; A comprehensive result analysis is conducted based on the evaluation results of the target evaluation students to obtain the evaluation information of the vocational education training results.

2. The vocational education training result evaluation method according to claim 1 is characterized in that: When determining students for vocational education training result evaluation, feature extraction is performed on the students participating in the vocational education training to obtain the first feature information of the students and obtain a set of student feature information. Then, the first feature information of the students is analyzed in the set of student feature information to determine whether the same feature exists. When the first feature information of the students has the same feature, the corresponding students are determined based on the same first feature information of the students, and the second feature is obtained for the corresponding students to obtain the second feature information of the students. Then, it is analyzed and determined whether the second feature information of the students is the same. If the second feature information of the students is the same, the third feature is obtained until the feature information is different.

3. The vocational education training result evaluation method according to claim 2 is characterized in that: Based on the goal evaluation, students acquire information through machine learning for educational practice, including: Conduct preliminary analysis and judgment on the education and training information to determine whether the education and training information is standardized and obtain preliminary analysis and judgment results; When there is non-standardization in the education and training information according to the preliminary analysis and judgment results, the non-standardized information is screened and standardized to obtain standardized education and training information; Determine the characteristic information of the target evaluation students and make the characteristic information clear; Based on the feature information, a machine learning model is used to identify the standardized educational training information, filter out the educational training information about the target evaluation students, and obtain the educational training information of the target students.

4. The vocational education training result evaluation method according to claim 3 is characterized in that: When identifying in the standardized educational training information based on the feature information through the machine learning model, the number of feature information is analyzed. When the number of feature information is unique, the feature information is the first feature information of the student. Based on the first feature information of the student, the machine learning model is used to identify, and the educational training information about the target evaluation student is screened out to obtain the educational training information of the target student; when the number of feature information is not unique, information set matching is performed according to the student's N-1th feature information. If a matching result exists, the matching result is identified based on the student's Nth feature information through the machine learning model, and the educational training information about the target evaluation student is screened out to obtain the educational training information of the target student. If no matching result exists, information set matching is performed according to the student's N-2th feature information, and it is analyzed whether there is a match. When a matching result exists, the machine learning model is used to identify based on the student's N-1th feature information, and the educational training information about the target evaluation student is screened out to obtain the identification and screening result. Then, the identification and screening result is marked and stored in combination with the student's N-1th feature information. At the same time, the identification is performed based on the student's Nth feature information through the machine learning model in the identification and screening result to obtain the educational training information of the target student.

5. The vocational education training result evaluation method according to claim 1 is characterized in that: The artificial intelligence evaluation and analysis model includes: a first evaluation unit, a second evaluation unit, a third evaluation unit and a summary evaluation unit; The first evaluation unit is used to identify and extract the student discipline record information in the target student education and training information, and evaluate and analyze the student discipline record information according to the discipline regulations to obtain a first evaluation result; The second evaluation unit is used to identify and extract the student practical learning information from the target student's educational training information to obtain a second evaluation result; The third evaluation unit is used to identify and extract the student assessment record information in the target student's education and training information, and to synthesize the individual assessment results based on the student assessment record information to obtain a third evaluation result; The summary evaluation unit is used to integrate data according to the first evaluation result, the second evaluation result and the third evaluation result to obtain the evaluation result of the target evaluation student.

6. The vocational education training result evaluation method according to claim 5 is characterized in that: The summary evaluation unit integrates data according to the first evaluation result, the second evaluation result and the third evaluation result, including: Obtaining occupational information for vocational education and training; Analyze occupational characteristics based on occupational information and determine the focus of vocational education and training; Conduct result weight analysis based on the focus of vocational education and training and determine the weight value; The first evaluation result, the second evaluation result and the third evaluation result are integrated with the weight value to determine the comprehensive evaluation value and obtain the evaluation result of the target evaluation student.

7. The vocational education training result evaluation method according to claim 1 is characterized in that: Conduct comprehensive results analysis based on the evaluation results of target evaluation students, including: Perform fuzzy evaluation conversion on the evaluation results of the target evaluation students to obtain the fuzzy evaluation results of the target evaluation students; Determine the distribution of vocational education training results according to the fuzzy evaluation results of the target evaluation students, and obtain the distribution results of vocational education training results; A comprehensive analysis is conducted based on the distribution of vocational education training results to obtain evaluation information on vocational education training results.

8. The vocational education training result evaluation method according to claim 7 is characterized in that: The vocational education training result evaluation information includes: text information and image information, and when a comprehensive analysis is performed according to the distribution results of the vocational education training results, it includes: Determine the image type of the image information and clarify the target image type; According to the distribution results of vocational education training results, images are generated according to the target image type to obtain vocational education training result evaluation image information; An intelligent analysis model is used to identify and analyze the image information of vocational education training results evaluation to obtain text information of vocational education training results evaluation.

9. The vocational education training result evaluation method according to claim 7 is characterized in that: After the image is generated according to the target image type based on the distribution results of the vocational education training results, image processing is also performed on the vocational education training results evaluation image information, including: According to the fuzzy evaluation results of the target evaluation students, information division processing is performed on the evaluation results of the target evaluation students to obtain multiple information data blocks; Establish the link relationship between the information data block and the fuzzy evaluation results of the target evaluation students, and determine the first-level link; The information data block is matched with the vocational education training result evaluation image information, and a link relationship between the information data block and the vocational education training result evaluation image information is established according to the matching result to determine the second-level link.

10. A vocational education training result evaluation system based on machine learning, characterized in that: The vocational education training result evaluation system includes: a target confirmation module, an information acquisition module, an intelligent analysis module and a comprehensive evaluation module; The target confirmation module is used to determine the students who will be evaluated for the vocational education training results and obtain the target evaluation students; The information acquisition module is used to acquire educational training information based on target evaluation students through machine learning to obtain educational training information of target students; The intelligent analysis module is used to use an artificial intelligence evaluation and analysis model to automatically evaluate the educational training information of the target students and obtain the evaluation results of the target evaluation students; The comprehensive evaluation module is used to perform comprehensive result analysis based on the evaluation results of the target evaluation students to obtain vocational education training result evaluation information.

Images